This standardization handbook was developed by the US Department of Defense with the assistance of the military departments, federal agencies, and industry. The most widely known and used reliability prediction handbook is MIL-217. It is used by both commercial companies and the defense industry, and is accepted and known world-wide. It contains failure rate models for numerous electronic components such as integrated circuits, transistors, diodes, resistors, capacitors, relays, switches, connectors and more, see RAM Commander Library.
Reliability predictions are an important tool for making design trade-off decisions and estimating future system reliability. They are often used for making initial product support decisions such as how many spares are required to support fielded systems. Inaccurate predictions can lead to overly conservative designs and/or excessive spare parts procurement resulting in additional life cycle cost (LCC).
MIL-HDBK-217 handbook includes a series of empirically based failure rate models covering virtually all electrical/electronic parts under 14 separate operational environments, such as ground fixed, airborne inhabited, etc. There are two primary prediction approaches: the Part Stress technique and the Parts Count technique. As their names imply, the Part Stress technique requires knowledge of the stress levels on each part to determine their failure rates, while the Parts Count technique assumes average stress levels to provide an early design estimate of the failure rates. Typical factors used to determine a part's failure rate include a temperature factor (πT), a power factor (πP), a power stress factor (πS), a quality factor (πQ), and an environmental factor (πE) in addition to the base failure rate (λb). For example, the failure rate model for a resistor is as follows:
λP = Part failure rate
λb = base failure rate, dependent on temperature and applied stress
π = acceleration factors for the used environmental application and other parameters that will affect the part reliability
πE = Environmental acceleration factor
πQ = quality acceleration factor
Other acceleration factors are modeled in terms of acceleration factor p. The data used to model these acceleration factors are mostly obtained from manufacturers data and field returned data. Using this method, it is possible to model the effects of using a component under certain environmental conditions, the effect of using specific methods of component quality screening, etc.
For equipment operating in multiple environments the calculations should be applied to a portion of the equipment in each environment.
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